This invention relates to a reference pattern adapting device for adapting a plurality of reference patterns to a plurality of adapted patterns by using a small number of training patterns. Such a reference pattern adapting device is particularly useful in a pattern recognition device, such as a speech recognition device.
Such a pattern recognition device is for recognizing an input pattern as a recognized pattern by selecting pertinent ones from a plurality of reference patterns which represent categories of recognition objects, respectively. The input pattern is represented by a group of input feature vectors. When the pattern recognition device is a speech recognition device, the input pattern represents speech found. Each of the reference patterns is defined by a signal model. Various signal models of the type are already known. By way of example, a signal model is described in an article contributed by L. R. Rabiner and B. H. Juang to the IEEE ASSP MAGAZINE, January 1986, pages 4 to 16, under the title of "An Introduction to Hidden Markov Models". As will be understood from the title of the Rabiner et al article, the signal model of Rabiner et al is called a hidden Markov model (HMM). The hidden Markov model carries out modeling of the reference pattern on the assumption that the group of the input feature vectors is produced by Markov stochastic process. The hidden Markov model is characterized by a plurality of states and transitions between the states. Each state produces a pattern vector in accordance with a predetermined probability distribution. Each transition is accompanied by a predetermined state transition probability. A distance between the input pattern and the reference pattern is given by a likelihood so that the hidden Markov model generates the group of the input feature vectors.
In the pattern recognition device wherein the hidden Markov model is used, it is desirable on obtaining a high recognition rate to utilize the reference pattern adapting device mentioned above. The reference pattern adapting device is for adapting a first through M-th reference patterns to first through M-th adapted patterns by using first through L-th training patterns which correspond to the first reference pattern through an L-th reference pattern, respectively, where M represents a first integer, L representing a second integer which is not greater than the first integer. The first through the M-th reference patterns are characterized by first through M-th reference pattern parameter groups and are represented by first through M-th groups of reference feature vectors. The first through the L-th training patterns are represented by first through L-th groups of training feature vectors.
By way of example, a speaker adapting device is described in an article contributed by Sadaoki Furui to the IEEE Transactions on Acoustics, Speech, and Signal Processing of United States, Volume ASSP-28, No. 2, pages 129 to 136, under the title of "A Training Procedure for Isolated Word Recognition System". According to the Furui article, the speaker adapting device adapts the first reference pattern through an L-th reference pattern to the first adapted pattern through an L-th adapted pattern among the first through the M-th adapted patterns by using the first through the L-th training patterns. The speaker adapting device requires a plurality of correspondence relations between the first through the M-th reference patterns to get an (L+1)-th adapted pattern through the M-th adapted pattern among the first through the M-th adapted patterns. As for remaining ones of the first through the M-th reference patterns, an (L+1)-th reference pattern through the M-th reference pattern are adapted to the (L+1)-th through the M-th adapted patterns by using the plurality of correspondence relations. In order to obtain the plurality of correspondence relations, it is necessary that a large number of speakers utter vocabularies which correspond to the first through the L-th training patterns.